방법 비교

	릿지 회귀(Ridge Regression)	Elastic Net	라쏘 회귀	주성분 분석
분야	머신러닝	머신러닝	머신러닝	머신러닝
계열	Machine learning	Machine learning	Machine learning	Machine learning
기원 연도	1970	2005	1996	2002
창시자	Hoerl, A.E. & Kennard, R.W.	Zou, H. & Hastie, T.	Tibshirani, R.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
유형	L2-regularized linear regression	Regularized linear regression (L1 + L2 penalty)	Regularized linear regression (L1 penalty)	Unsupervised dimensionality reduction
원전	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.	Zou, H. & Hastie, T. (2005). Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society: Series B, 67(2), 301–320.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
별칭	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization	Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
관련	4	4	4	3
요약	Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated.	Elastic Net is a regularized linear regression method introduced by Zou and Hastie in 2005 that blends the LASSO (L1) and Ridge (L2) penalties, so it performs variable selection and coefficient shrinkage at the same time. It is designed for predictive and explanatory modelling on data with many, possibly correlated, predictors.	Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter.	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.